Abstract
Analyzing connected data sets such as social networks, electrical grids, and biological networks requires running complex algorithms on graph data structures. We will discuss graph algorithms as simple as breadth first search to advanced algorithms such as community detection, unsupervised clustering, and semisupervised learning with belief propagation. High performance implementations of these algorithms cannot be easily written in traditional high level languages, but can be written in Julia due to novel programming language properties. I’ll speak about recent research in applied graph theory and its relationship to the two language problem and discuss how the JuliaGraphs ecosystem tackles these problems. This enables further expansions of data science research in diverse applications.
Bio
James Fairbanks earned his PhD at Georgia Tech in 2016 and is a Research Engineer at the Georgia Tech Research Institute. He studies Data Analysis and High Performance Computing and applications in healthcare, social science, and national security. He has studies social media modeling combining numerical, statistical, and streaming algorithms for graph analysis. James has conducted research at government, academic, and national lab institutions. He is also a core maintainer of the Julia package LightGraphs which is integrated in the commercial product JuliaPro, and has been used in research projects in systems biology, mathematical optimization, and other scientific domains.